Homework Assignment #3

questions

1. Which option do you plan to pursue?

• I plan to pursue the 2nd option, the infographic

2. Restate your question. Has it changed at all since HW #1? If yes, how so?

• A lot of information that comes out about wildfires focuses on their size and severity, but I’ve never seen much information about the temporal component of wildfires. What time of day are fires spotted or contained? When is “fire season”? How long does it take to contain a fire? This led me to change my question to “When are wildland firefighters working?” (need some help making this better), where I’ll be focusing on some of the seemingly-overlooked temporal aspects of wildfires. My first sub-question is “What hour of the day are wildfires discovered or contained?”. The next is “How long does it take to contain a fire?”. The next is “When are fires active during the year?”.

3. Explain which variables from your data set you will use to answer your question.

• The variables I’ll use will be discovery_doy (day of the year from 1-365 the fire was discovered), discovery_time (time of the day the fire was discovered), cont_doy (day of the year from 1-365 the fire was contained), cont_time (time of day the fire was contained), discovery_date, and cont_date. From these variables I was able to create a new variable binning the times into hour of the day (0-23). With this, I can visualize hour of the day on the x-axis and count of fires discovered/contained within each hour on the y-axis in a barplot. This helps to answer “What time of day are fires discovered/contained?”. From the discovery and contained dates I was able to derive the number of days that each fire was active. With this, I will plot a subset of the largest fires in the dataset and the number of days that each was active to give a sense of how long it takes to contain a large fire. Lastly, the day of the year from 1-365 variables will be helpful in plotting with bars the count of fires contained/discovered on each day. This will show when the most fires are occurring during the year.

Inspiration

I really like the plot style in this. I guess it’s a lollipop plot of sorts, but I like the line segments with different positions based on their start on the x-axis value. I plan to use it to compare fire duration (days active) between different fires while also showing what time of year they occurred.

I was looking up wildfire infographics and liked the color scheme in this one. The black background with fire-colored plot aesthetics is punchy and contrasty. I plan to use the graphic idea in this one for the two barplots that I create.

Rough sketches

knitr::include_graphics(here("images", "draft_1.jpg"))

knitr::include_graphics(here("images", "draft_2.jpg"))

knitr::include_graphics(here("images", "draft_3.jpg"))

Visualization mock-ups

# read in california fires
ca_fires <- read_csv(here("data", "ca_fires.csv"))

Data wrangling, cleaning, and subsetting

## cleaning and wrangling
ca_fires_clean <- ca_fires %>% 
  # convert names to lower snakecase
  janitor::clean_names() %>% 
  # select variables of interest
  select(nwcg_reporting_agency, nwcg_reporting_unit_name, fire_name, fire_year, discovery_date, discovery_doy, discovery_time, discovery_time, nwcg_cause_classification, nwcg_general_cause, cont_date, cont_doy, cont_time, fire_size, fire_size_class, fips_name) %>% 
  # create a new column to describe days it took to contain fire 
  mutate(days_active = as.numeric(difftime(cont_date, discovery_date, units = "days"))) %>% 
  # remove the minutes from each discovery and contained time to get just the hour
  mutate(disc_hour = str_sub(discovery_time, start = 1, end = -3),
         cont_hour = str_sub(cont_time, start = 1, end = -3))
           
           
## create new dataframes summarizing number of fires discovered and contained on each day of the year
disc_doy <- ca_fires_clean %>% 
  group_by(discovery_doy) %>% 
  summarize(count = n())

cont_doy <- ca_fires_clean %>% 
  group_by(cont_doy) %>% 
  summarize(count = n()) %>% 
  drop_na()

## create a new dataframe subsetting the largest fires
largest_fires <- ca_fires_clean %>% 
  slice_max(fire_size, n = 11) %>%
  # remove the Thomas Fire for lack of data
  filter(!fire_name %in% c("THOMAS")) %>% 
  # assign a number to each fire for plotting purposes
  mutate(fire_num = rank(desc(fire_size))) %>% 
  # turn fire names into title case and add "Fire" after
  mutate(fire_name = str_to_title(fire_name),
         fire_name = paste(fire_name, "Fire")) %>% 
  # add commas to fire size and create a new column that adds the acres label
  mutate(fire_size = format(fire_size, big.mark = ",", scientific = FALSE),
         acres_label = paste(fire_size, "acres")) %>% 
  # separat the month and day from the year
  mutate(disc_a = format(discovery_date, "%m-%d"),
         cont_a = format(cont_date, "%m-%d"))

# add dummy year to discovery and contained dates for plotting
largest_fires$disc_a <- mdy(paste0(largest_fires$disc_a, "-2000"))
largest_fires$cont_a <- mdy(paste0(largest_fires$cont_a, "-2000"))

##
# group fires by discovery and contained hour and get counts
disc_hour <- ca_fires_clean %>% 
  group_by(disc_hour) %>% 
  summarize(count = n()) %>% 
  drop_na()

cont_hour <- ca_fires_clean %>% 
  group_by(cont_hour) %>% 
  summarize(count = n()) %>% 
  drop_na()

Plotting

What time of day are fires discovered?

# barplot of count of fires discovered at each hour of the day
  ggplot() +
  # plot barplot with counts
  geom_bar(data = disc_hour,
           aes(x = disc_hour, y = count, fill = count),
           stat = "identity") +
  # color bars by gradient for fire aesthetic
  scale_fill_gradient(low = "orange", high = "red") +
  # modify y-axis limit to make data flush with x-axis
  scale_y_continuous(expand = c(0,0),
                     limits = c(0, 19000)) +
  # add labels, title, and subtitle
  labs(x = "hour of the day",
       y = "number of fires",
       title = "When are fires spotted?",
       subtitle = "Count of wildfires discovered at each hour of the day from 1992-2018") +
  # modify theme elements
  theme(panel.grid.major.x = element_blank(), # remove x-axis grid lines
        # set plot background to be black
        plot.background = element_rect(fill = "black", color = NA),
        # set panel background to be black
        panel.background = element_rect(fill = "black", color = NA),
        # set panel border to be white
        panel.border = element_rect(color = "white", fill = NA),
        # set axis labels to be white
        axis.title = element_text(color = "white",
                                  family = "kanit",
                                  size = 12),
        # set axis breaks to be white
        axis.text = element_text(color = "white",
                                 family = "kanit"),
        # modify title text
        plot.title = element_text(color = "white",
                                  family = "kanit",
                                  face = "bold",
                                  size = 15),
        # modify subtitle text
        plot.subtitle = element_text(color = "white",
                                     family = "kanit",
                                     size = 12),
        # set axis ticks to be white
        axis.ticks = element_line(color = "white"),
        # set axis lines to be white
        axis.line = element_line(color = "white"),
        # remove the legend
        legend.position = "none")

When is “fire season”?

# barplot of discovery day of the year
disc_doy %>% 
  ggplot() +
  # plot the counts dataframe with stat = "identity"
  geom_bar(aes(x = discovery_doy, y = count, fill = count),
           stat = "identity", color = "orange", linewidth = .05) +
  # set a gradient fill on the bars
  scale_fill_gradient(low = "orange", high = "red") +
  # make data flush with the x- and y-axes
  scale_x_continuous(expand = c(0,0)) +
  scale_y_continuous(expand = c(0,0),
                     limits = c(0, 3200)) +
  # set x-axis and y-axis labels
  labs(x = "day of the year",
       y = "number of fires",
       title = "When is 'fire season'?",
       subtitle = "Count of wildfires discovered on each day of the year from 1992-2018") +
  # add annotations to label spikes in fire activity
  annotate(geom = "text", x = 180, y = 2850, size = 3, 
           label = "4th of July", color = "white", angle = 90,
           family = "kanit") +
  annotate(geom = "text", x = 240, y = 1800, size = 3, 
           label = "Labor Day", color = "white", angle = 90,
           family = "kanit") +
  annotate(geom = "text", x = 167, y = 1800, size = 3, 
           label = "Summer Solstice", color = "white", angle = 90,
           family = "kanit") +
    # modify theme elements
  theme(panel.grid.major = element_line(color = NA), # remove grid lines
        panel.grid.minor = element_line(color = NA),
        # set plot background to be black
        plot.background = element_rect(fill = "black", color = NA),
        # set panel background to be black
        panel.background = element_rect(fill = "black", color = NA),
        # set panel border to be white
        panel.border = element_rect(color = "white", fill = NA),
        # modify axis labels text
        axis.title = element_text(color = "white",
                                  family = "kanit",
                                  size = 12),
        # modify axis breaks text
        axis.text = element_text(color = "white",
                                 family = "kanit"),
        # set axis ticks to be white
        axis.ticks = element_line(color = "white"),
        # set axis lines to be white
        axis.line = element_line(color = "white"),
        # remove the legend
        legend.position = "none",
        # modify plot title text
        plot.title = element_text(color = "white",
                                  family = "kanit",
                                  size = 15,
                                  face = "bold"),
        # modify plot subtitle text
        plot.subtitle = element_text(color = "white",
                                     family = "kanit",
                                     size = 12))

How long does it take to contain a megafire?

# not even sure what type of plot this is
ggplot(data = largest_fires) +
  # plot each fire as a line segment, length-dependent on days active
  geom_segment(aes(x = disc_a, xend = cont_a, y = fire_num, 
                   yend = fire_num, color = days_active), linewidth = 1) +
  # color segments in a gradient fill based on fire size
  scale_color_gradient(low = "orange", high = "red") +
  # directly label each fire with fire name
  geom_text(aes(x = cont_a, y = fire_num, label = fire_name), 
            hjust = 1, vjust = -.7, size = 4, 
            color = "white", family = "kanit") +
  # directly label each fire with fire size
  geom_text(aes(x = cont_a, y = fire_num, label = acres_label), 
            hjust = 1, vjust = 1.5, size = 4, 
            nudge_x = 0.5, color = "white", family = "kanit") +
  # convert date format into month names
  scale_x_date(date_breaks = "1 month", 
               date_labels = "%b") +
  # this line was meant to give the bars some space but somehow orders them by fire size so I'm scared to touch it
  scale_y_discrete(limits = c(0, 11),
                   expand = c(-1,-1)) +
  # add axis labels, title, and subtitle
  labs(x = "fire duration",
       y = "",
       title = "How long does it take to contain a megafire?",
       subtitle = "10 of California's largest megafires, mapped by days active") +
  theme(panel.grid.major.x = element_blank(), # remove x-axis grid lines
        # remove panel grid lines
        panel.grid.major = element_line(color = NA),
        panel.grid.minor = element_line(color = NA),
        # set plot background to be black
        plot.background = element_rect(fill = "black", color = NA),
        # set panel background to be black
        panel.background = element_rect(fill = "black", color = NA),
        # set panel border to be white
        panel.border = element_rect(color = "white", fill = NA),
        # modify axis title text
        axis.title = element_text(color = "white",
                                  family = "kanit",
                                  size = 12),
        # modify axis breaks text
        axis.text = element_text(color = "white",
                                 family = "kanit"),
        # set axis ticks to be white
        axis.ticks = element_line(color = "white"),
        # remove y-axis ticks
        axis.ticks.y = element_blank(),
        # remove y-axis labels
        axis.text.y = element_blank(),
        # set axis lines to be white
        axis.line = element_line(color = "white"),
        # remove the legend
        legend.position = "none",
        # format plot title
        plot.title = element_text(color = "white",
                                  family = "kanit",
                                  size = 15,
                                  face = "bold"),
        # format plot subtitle
        plot.subtitle = element_text(color = "white",
                                     family = "kanit",
                                     size = 12))

Follow-up questions

1. What challenges did you encounter or anticipate encountering as you continue to build / iterate on your visualizations in R?

• So. many. challenges. One that sticks out is trying to force a more advanced graphic because I thought a barplot might be a bit simple. With my barplot showing count of fires by hour of the day in which they were discovered, I initially used a circular barplot so that it could look like a clock. I quickly realize that a) clocks go from 1-12, not 0-23 and b) circular barplots make it almost impossible to compare between bars. I decided that the simple plot was the more effective way to show the information.

2. What ggplot extension tools / packages do you need to use to build your visualizations? Are there any that we haven’t covered in class that you’ll be learning how to use for your visualizations?

• Nothing fancy. I just used showtext to be able to load in Google fonts.

3. What feedback do you need from the instructional team and / or your peers to ensure that your intended message is clear?

• How much additional text to add! When I was making all my plots, I realized I could continue adding asterisks and additional information, but I figured all that stuff would be better in the body of an infographic/in between plots, rather than right on the plot.